Abstract. Global and regional sources and sinks of carbon across the earth's
surface have been studied extensively using atmospheric carbon dioxide
(CO2) observations and atmospheric chemistry-transport model (ACTM)
simulations (top-down/inversion method). However, the uncertainties in the
regional flux distributions remain unconstrained due to the lack of
high-quality measurements, uncertainties in model simulations, and
representation of data and flux errors in the inversion systems. Here, we
assess the representation of data and flux errors using a suite of 16
inversion cases derived from a single transport model (MIROC4-ACTM) but
different sets of a priori (bottom-up) terrestrial biosphere and oceanic
fluxes, as well as prior flux and observational data uncertainties (50
sites) to estimate CO2 fluxes for 84 regions over the period 2000–2020.
The inversion ensembles provide a mean flux field that is consistent with
the global CO2 growth rate, land and ocean sink partitioning of
−2.9 ± 0.3 (± 1σ uncertainty on the ensemble mean) and
−1.6 ± 0.2 PgC yr−1, respectively, for the period 2011–2020
(without riverine export correction), offsetting about 22 %–33 % and
16 %–18 % of global fossil fuel CO2 emissions. The rivers carry about
0.6 PgC yr−1 of land sink into the deep ocean, and thus the effective
land and ocean partitioning is −2.3 ± 0.3 and −2.2 ± 0.3,
respectively. Aggregated fluxes for 15 land regions compare reasonably well
with the best estimations for the 2000s (∼ 2000–2009), given
by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all
regions appeared as a carbon sink over 2011–2020. Interannual variability
and seasonal cycle in CO2 fluxes are more consistently derived for two
distinct prior fluxes when a greater degree of freedom (increased prior flux
uncertainty) is given to the inversion system. We have further evaluated the
inversion fluxes using meridional CO2 distributions from independent
(not used in the inversions) aircraft and surface measurements, suggesting
that the ensemble mean flux (model–observation mean ± 1σ
standard deviation = −0.3 ± 3 ppm) is best suited for global and
regional CO2 flux budgets than an individual inversion
(model–observation 1σ standard deviation = −0.35 ± 3.3 ppm).
Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean
regions at 5-year intervals, we show promise in the capability to track flux
changes toward supporting the ongoing and future CO2 emission
mitigation policies.